Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are.
Ranked #5 on Action Spotting on SoccerNet
This paper aims to bring a new lightweight yet powerful solution for the task of Emotion Recognition and Sentiment Analysis.
We introduce the AVECL-UMons dataset for audio-visual event classification and localization in the context of office environments.
Understanding expressed sentiment and emotions are two crucial factors in human multimodal language.
Ranked #1 on Multimodal Sentiment Analysis on CMU-MOSEI
As new data-sets for real-world visual reasoning and compositional question answering are emerging, it might be needed to use the visual feature extraction as a end-to-end process during training.
Even with the growing interest in problems at the intersection of Computer Vision and Natural Language, grounding (i. e. identifying) the components of a structured description in an image still remains a challenging task.
When searching for an object humans navigate through a scene using semantic information and spatial relationships.
So far, the goal has been to maximize scores on automated metric and to do so, one has to come up with a plurality of new modules and techniques.
This paper describes the UMONS solution for the Multimodal Machine Translation Task presented at the third conference on machine translation (WMT18).
no code implementations • 19 Jan 2018 • Matei Mancas, Christian Frisson, Joëlle Tilmanne, Nicolas D'Alessandro, Petr Barborka, Furkan Bayansar, Francisco Bernard, Rebecca Fiebrink, Alexis Heloir, Edgar Hemery, Sohaib Laraba, Alexis Moinet, Fabrizio Nunnari, Thierry Ravet, Loïc Reboursière, Alvaro Sarasua, Mickaël Tits, Noé Tits, François Zajéga, Paolo Alborno, Ksenia Kolykhalova, Emma Frid, Damiano Malafronte, Lisanne Huis in't Veld, Hüseyin Cakmak, Kevin El Haddad, Nicolas Riche, Julien Leroy, Pierre Marighetto, Bekir Berker Türker, Hossein Khaki, Roberto Pulisci, Emer Gilmartin, Fasih Haider, Kübra Cengiz, Martin Sulir, Ilaria Torre, Shabbir Marzban, Ramazan Yazıcı, Furkan Burak Bâgcı, Vedat Gazi Kılı, Hilal Sezer, Sena Büsra Yenge, Charles-Alexandre Delestage, Sylvie Leleu-Merviel, Muriel Meyer-Chemenska, Daniel Schmitt, Willy Yvart, Stéphane Dupont, Ozan Can Altiok, Aysegül Bumin, Ceren Dikmen, Ivan Giangreco, Silvan Heller, Emre Külah, Gueorgui Pironkov, Luca Rossetto, Yusuf Sahillioglu, Heiko Schuldt, Omar Seddati, Yusuf Setinkaya, Metin Sezgin, Claudiu Tanase, Emre Toyan, Sean Wood, Doguhan Yeke, Françcois Rocca, Pierre-Henri De Deken, Alessandra Bandrabur, Fabien Grisard, Axel Jean-Caurant, Vincent Courboulay, Radhwan Ben Madhkour, Ambroise Moreau
The 11th Summer Workshop on Multimodal Interfaces eNTERFACE 2015 was hosted by the Numediart Institute of Creative Technologies of the University of Mons from August 10th to September 2015.
We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normalization, in order to compute the most informative image features for our task.
In Multimodal Neural Machine Translation (MNMT), a neural model generates a translated sentence that describes an image, given the image itself and one source descriptions in English.
In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation.